Hello, all. I’m happy to introduce my research blog. This is where I will write about my research projects and other related stuff that I find interesting.
Hi, again. This is my first blog post. The idea here is to show a little of my research is a different way to the traditional papers that we read. I hope you can enjoy reading it as I did when I wrote this.
Recently I started to be interested in visualising evolutionary algorithms (EAs). Here, I will summarise some fascinating works that helped me create an interest in the field of study.
The main idea of visualising EAs is to show efficient ways of communicating the behaviour of such algorithms. What is remarkable about looking at the images is that we can look at them, which feels a little less abstract. I’m sure you saw some of these tools, and one of the most common is the fitness landscapes.
In a population-based algorithm:
In a gradient descent algorithm (commonly used in Neural Networks and other ML algorithms):
In case this is your first time hearing about the fitness landscape, let me give a summary of what they are:
Are you interested? Take a look at this Wikipedia page: https://en.wikipedia.org/wiki/Evolutionary_landscape.
Or watch this YouTube video: https://www.youtube.com/watch?v=4pdiAneMMhU
That’s super, isn’t it? These methods were developed for singleobjective algorithms, and we don’t have yet many tools to visualize multiobjective EAs. Having tools like this can help us to create new and (hopefully) better algorithms. That is because we can see how the process changes over time, and then we an study the strengths and weaknesses of algorithms. And by knowing these strengths and weaknesses we can improve existing algorithms or even create new and better EAs.